The exact task of feature attribution can be interpreted in different ways, leading to some discussion about which properties a feature attribution method should satisfy [1]}. Feature attribution methods can roughly be divided in four categories using two properties: local vs. global, and model- vs. data-centric. The first property concerns the locality of the explanation: in local feature attribution, we map the importance of features in a given sample, whereas in global feature attribution, we map the importance of features for all samples in a dataset (also called feature importance). The second property concerns the target of the explanation: in model-centric feature attribution, we are interested in the importance of features for a specific model, whereas in data-centric feature attribution, we try to measure the informativeness of features in the data, independent of any specific model. This can be estimated using classical statistical or information-theoretical techniques [2]}. Model- and data-centric feature attributions are not necessarily the same, as a model can often make predictions using only a subset of the informative features, or even using features that are generally non-informative (in which case the model is overfitting). In this work, we specifically evaluate local, model-centric feature attributions.
